gemini-code-assist[bot] commented on code in PR #37917: URL: https://github.com/apache/beam/pull/37917#discussion_r2983828847
########## sdks/python/apache_beam/ml/inference/agent_development_kit_test.py: ########## @@ -0,0 +1,339 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# pytype: skip-file + +import asyncio +import unittest +from unittest import mock + +try: + from google.adk.agents.llm_agent import Agent Review Comment:  For consistency with the main `agent_development_kit.py` module, which imports `Agent` directly from `google.adk.agents`, this test file should also import `Agent` from `google.adk.agents` instead of `google.adk.agents.llm_agent`. This ensures that the test uses the same `Agent` class definition as the module it's testing. ```suggestion from google.adk.agents import Agent ``` ########## sdks/python/apache_beam/ml/inference/agent_development_kit.py: ########## @@ -0,0 +1,281 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +"""ModelHandler for running agents built with the Google Agent Development Kit. + +This module provides :class:`ADKAgentModelHandler`, a Beam +:class:`~apache_beam.ml.inference.base.ModelHandler` that wraps an ADK +:class:`google.adk.agents.llm_agent.LlmAgent` so it can be used with the +:class:`~apache_beam.ml.inference.base.RunInference` transform. + +Typical usage:: + + import apache_beam as beam + from apache_beam.ml.inference.base import RunInference + from apache_beam.ml.inference.agent_development_kit import ADKAgentModelHandler + from google.adk.agents import LlmAgent + + agent = LlmAgent( + name="my_agent", + model="gemini-2.0-flash", + instruction="You are a helpful assistant.", + ) + + with beam.Pipeline() as p: + results = ( + p + | beam.Create(["What is the capital of France?"]) + | RunInference(ADKAgentModelHandler(agent=agent)) + ) + +If your agent contains state that is not picklable (e.g. tool closures that +capture unpicklable objects), pass a zero-arg factory callable instead:: + + handler = ADKAgentModelHandler(agent=lambda: LlmAgent(...)) + +""" + +import asyncio +import logging +import uuid +from collections.abc import Callable +from collections.abc import Iterable +from collections.abc import Sequence +from typing import Any +from typing import Optional +from typing import Union + +from apache_beam.ml.inference.base import ModelHandler +from apache_beam.ml.inference.base import PredictionResult + +try: + from google.adk.agents import Agent + from google.adk.runners import Runner + from google.adk.sessions import BaseSessionService + from google.adk.sessions import InMemorySessionService + from google.genai.types import Content as genai_Content + from google.genai.types import Part as genai_Part + ADK_AVAILABLE = True +except ImportError: + ADK_AVAILABLE = False + genai_Content = Any # type: ignore[assignment, misc] + genai_Part = Any # type: ignore[assignment, misc] + +LOGGER = logging.getLogger("ADKAgentModelHandler") + +# Type alias for an agent or factory that produces one +_AgentOrFactory = Union["Agent", Callable[[], "Agent"]] + + +class ADKAgentModelHandler(ModelHandler[Union[str, genai_Content], + PredictionResult, + "Runner"]): + """ModelHandler for running ADK agents with the Beam RunInference transform. + + Accepts either a fully constructed :class:`google.adk.agents.Agent` or a + zero-arg factory callable that produces one. The factory form is useful when + the agent contains state that is not picklable and therefore cannot be + serialized alongside the pipeline graph. + + Each call to :meth:`run_inference` invokes the agent once per element in the + batch. By default every invocation uses a fresh, isolated session (stateless). + Stateful multi-turn conversations can be achieved by passing a ``session_id`` + key inside ``inference_args``; elements sharing the same ``session_id`` will + continue the same conversation history. + + Args: + agent: A pre-constructed :class:`~google.adk.agents.Agent` instance, or a + zero-arg callable that returns one. The callable form defers agent + construction to worker ``load_model`` time, which is useful when the + agent cannot be serialized. + app_name: The ADK application name used to namespace sessions. Defaults to + ``"beam_inference"``. + session_service_factory: Optional zero-arg callable returning a + :class:`~google.adk.sessions.BaseSessionService`. When ``None``, an + :class:`~google.adk.sessions.InMemorySessionService` is created + automatically. + min_batch_size: Optional minimum batch size. + max_batch_size: Optional maximum batch size. + max_batch_duration_secs: Optional maximum time to buffer a batch before + emitting; used in streaming contexts. + max_batch_weight: Optional maximum total weight of a batch. + element_size_fn: Optional function that returns the size (weight) of an + element. + """ + def __init__( + self, + agent: _AgentOrFactory, + app_name: str = "beam_inference", + session_service_factory: Optional[Callable[[], + "BaseSessionService"]] = None, + *, + min_batch_size: Optional[int] = None, + max_batch_size: Optional[int] = None, + max_batch_duration_secs: Optional[int] = None, + max_batch_weight: Optional[int] = None, + element_size_fn: Optional[Callable[[Any], int]] = None, + **kwargs): + if not ADK_AVAILABLE: + raise ImportError( + "google-adk is required to use ADKAgentModelHandler. " + "Install it with: pip install google-adk") + + if agent is None: + raise ValueError("'agent' must be an Agent instance or a callable.") + + self._agent_or_factory = agent + self._app_name = app_name + self._session_service_factory = session_service_factory + + super().__init__( + min_batch_size=min_batch_size, + max_batch_size=max_batch_size, + max_batch_duration_secs=max_batch_duration_secs, + max_batch_weight=max_batch_weight, + element_size_fn=element_size_fn, + **kwargs) + + def load_model(self) -> "Runner": + """Instantiates the ADK Runner on the worker. + + Resolves the agent (calling the factory if a callable was provided), then + creates a :class:`~google.adk.runners.Runner` backed by the configured + session service. + + Returns: + A fully initialised :class:`~google.adk.runners.Runner`. + """ + if callable(self._agent_or_factory) and not isinstance( + self._agent_or_factory, Agent): + agent = self._agent_or_factory() + else: + agent = self._agent_or_factory + + if self._session_service_factory is not None: + session_service = self._session_service_factory() + else: + session_service = InMemorySessionService() + + runner = Runner( + agent=agent, + app_name=self._app_name, + session_service=session_service, + ) + LOGGER.info( + "Loaded ADK Runner for agent '%s' (app_name='%s')", + agent.name, + self._app_name, + ) + return runner + + def run_inference( + self, + batch: Sequence[Union[str, genai_Content]], + model: "Runner", + inference_args: Optional[dict[str, Any]] = None, + ) -> Iterable[PredictionResult]: + """Runs the ADK agent on each element in the batch. + + Each element is sent to the agent as a new user turn. The final response + text from the agent is returned as the ``inference`` field of a + :class:`~apache_beam.ml.inference.base.PredictionResult`. + + Args: + batch: A sequence of inputs, each of which is either a ``str`` (the user + message text) or a :class:`google.genai.types.Content` object (for + richer multi-part messages). + model: The :class:`~google.adk.runners.Runner` returned by + :meth:`load_model`. + inference_args: Optional dict of extra arguments. Supported keys: + + - ``"session_id"`` (:class:`str`): If supplied, all elements in this + batch share this session ID, enabling stateful multi-turn + conversations. If omitted, each element receives a unique auto- + generated session ID. + - ``"user_id"`` (:class:`str`): The user identifier to pass to the + runner. Defaults to ``"beam_user"``. + + Returns: + An iterable of :class:`~apache_beam.ml.inference.base.PredictionResult`, + one per input element. + """ + if inference_args is None: + inference_args = {} + + user_id: str = inference_args.get("user_id", "beam_user") + + results = [] + for element in batch: + session_id: str = inference_args.get("session_id", str(uuid.uuid4())) + + # Ensure a session exists for this invocation + model.session_service.create_session( + app_name=self._app_name, + user_id=user_id, + session_id=session_id, + ) + + # Wrap plain strings in a Content object + if isinstance(element, str): + message = genai_Content(role="user", parts=[genai_Part(text=element)]) + else: + # Assume the caller has already constructed a types.Content object + message = element + + response_text = asyncio.run( + self._invoke_agent(model, user_id, session_id, message)) + + results.append( + PredictionResult( + example=element, + inference=response_text, + model_id=model.agent.name, + )) + Review Comment:  Calling `asyncio.run` repeatedly within a loop can be inefficient as it creates and closes a new event loop for each call. For better performance and resource management, especially in a batch processing context, it's recommended to run all asynchronous operations concurrently within a single event loop using `asyncio.gather`. This refactoring collects all agent invocations and then executes them in parallel, which is more efficient for batch processing. ```python agent_invocations = [] elements_with_sessions = [] for element in batch: session_id: str = inference_args.get("session_id", str(uuid.uuid4())) # Ensure a session exists for this invocation model.session_service.create_session( app_name=self._app_name, user_id=user_id, session_id=session_id, ) # Wrap plain strings in a Content object if isinstance(element, str): message = genai_Content(role="user", parts=[genai_Part(text=element)]) else: # Assume the caller has already constructed a types.Content object message = element agent_invocations.append(self._invoke_agent(model, user_id, session_id, message)) elements_with_sessions.append(element) # Run all agent invocations concurrently response_texts = asyncio.run(asyncio.gather(*agent_invocations)) results = [] for i, element in enumerate(elements_with_sessions): results.append( PredictionResult( example=element, inference=response_texts[i], model_id=model.agent.name, )) ``` -- This is an automated message from the Apache Git Service. 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